Neural networks help cardiac diagnostics

Researchers at Uppsala University and heart specialists in Brazil have developed an AI that automatically diagnoses atrial fibrillation and five other common ECG abnormalities just as well as a cardiologist.

Photo
The new study demonstrates that an AI is capable of automatically diagnosing the abnormalities indicated by an ECG.
Source: Uppsala University

An electrocardiogram (ECG) is a simple test that can be used to check the heart's rhythm and electrical activity. The results are shown on a graph that can reveal various conditions that affect the heart. The tool is routinely used in healthcare and each ECG needs to be interpreted manually by a cardiologist.

The new study demonstrates that an AI is capable of automatically diagnosing the abnormalities indicated by an ECG. The AI was initially trained on a database comprising over two million ECGs that had already been diagnosed manually. In this way, it can learn to recognise typical patterns for the six most common ECG abnormalities and then make a diagnosis of another patient with one of these conditions – with the same precision as a cardiologist.

Great potential for improved cardiovascular care

The method is currently not ready for use in clinics and hospitals; however, the researchers believe that it offers great potential for improved cardiovascular care in low and middle-income countries where large parts of the population lack the same level of access to specialists who are able to interpret ECG results as we enjoy here in Sweden.

“This is the first result of a collaboration that we have built up over the past two years. I have great confidence that in the future this type of deep collaboration between AI researchers and medical researchers will be able to create new knowledge that can help people enjoy a better quality of life,” says Thomas Schön, professor of automatic control, who works in machine learning and AI at Uppsala University and was responsible for the technical part of the study.

Computers learn to solve tasks

The mathematical model (known as a deep artificial neural network) on which the study is based is a good example of the basic concept behind machine learning, where computers build their own model and then use it to learn to solve tasks based on collected data. The method differs from the classic method of working with a computer where the computer is manually programmed to perform a very specific task. The results for many problems have proved to be better when machine learning is used and the computer itself is allowed to identify patterns from gathered figures, texts, diagrams and images.

Subscribe to our newsletter

Related articles

Sensor predicts worsening heart failure before hospitalization

Sensor predicts worsening heart failure before hospitalization

A wearable sensor could help doctors remotely detect critical changes in heart failure patients days before a health crisis occurs and could prevent hospitalization.

AI used to screen for FASD

AI used to screen for FASD

Scientists have developed a new tool that can screen children for fetal alcohol spectrum disorder (FASD) quickly and affordably.

Affordable wearable AI heart monitor

Affordable wearable AI heart monitor

A Cambridge start-up has developed a low-cost next-generation wearable heart and cardiovascular function monitor which uses AI to diagnose heart rhythm and respiratory problems in real time.

Microfluidics and AI microscopy measure hemoglobin

Microfluidics and AI microscopy measure hemoglobin

Researchers at the Indian Institute of Science and SigTuple Technologies have developed a method to measure hemoglobin levels in small-volume blood samples.

Explainable AI for decoding genome biology

Explainable AI for decoding genome biology

Researchers have developed advanced explainable AI in a technical tour de force to decipher regulatory instructions encoded in DNA.

AI identifies 'ugly ducklings' to catch skin cancer

AI identifies 'ugly ducklings' to catch skin cancer

Deep learning-based system enables dermatologist-level identification of suspicious skin lesions from smartphone photos, allowing better screening.

Designing medical deep learning systems

Designing medical deep learning systems

Researchers have analysed whether better design of deep learning studies can lead to the faster transformation of medical practices.

'Liquid' machine learning system adapts to changing conditions

'Liquid' machine learning system adapts to changing conditions

A machine learning system learns on the job. By continuously adapting to new data inputs, this “liquid network” could aid decision-making in medical diagnosis.

Diagnosing prostate cancer using biosensors and AI

Diagnosing prostate cancer using biosensors and AI

Successful precision cancer diagnosis through an AI analysis of multiple factors of prostate cancer. Potential application of the precise diagnoses of other cancers by utilizing a urine test.

Popular articles